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Comparing Probability Forecasts in Markov Regime Switching Business Cycle Models

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  • Daniel R. Smith
  • Allan Layton

Abstract

We evaluate techniques for comparing the ability of Markov regime switching (MRS) models to fit underlying regimes of a series of interest. This is particularly important in the business cycle literature where one may be interested in determining whether using leading indicators to allow transition probabilities to vary improves the ability of MRS models to fit the NBER business cycle chronology. This is typically done using the quadratic probability score, or QPS (Diebold and Rudebusch, 1989). Although it is possible to statistically compare the QPS statistics for two MRS models using the Diebold and Mariano (1995) (DM) test statistic for comparing forecasts, we find using a Monte Carlo experiment that the DM statistic tends to under-reject (the null of "no difference in forecast accuracy") when comparing MRS models. This we believe is because of the strong non-normality of the forecast errors of such models. Furthermore, using simulation-based inference we demonstrate that leading indicators improve the fit of an MRS model of the US business cycle chronology by 24 percent, such improvement having a p-value of 0.001.

Suggested Citation

  • Daniel R. Smith & Allan Layton, 2007. "Comparing Probability Forecasts in Markov Regime Switching Business Cycle Models," Journal of Business Cycle Measurement and Analysis, OECD Publishing, Centre for International Research on Economic Tendency Surveys, vol. 2007(1), pages 79-98.
    • Handle: RePEc:oec:stdkaa:5l4kx1hddhjb
    DOI: 10.1787/jbcma-v2007-art4-en
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    Cited by:

    1. Layton, Allan P. & Smith, Daniel R., 2007. "Business cycle dynamics with duration dependence and leading indicators," Journal of Macroeconomics, Elsevier, vol. 29(4), pages 855-875, December.
    2. Klaus Abberger & Wolfgang Nierhaus, 2010. "Markov-Switching and the Ifo Business Climate: the Ifo Business Cycle Traffic Lights," OECD Journal: Journal of Business Cycle Measurement and Analysis, OECD Publishing, Centre for International Research on Economic Tendency Surveys, vol. 2010(2), pages 1-13.
    3. Yin Liao & John Stachurski, 2011. "Parametric Conditional Monte Carlo Density Estimation," ANU Working Papers in Economics and Econometrics 2011-562, Australian National University, College of Business and Economics, School of Economics.
    4. Luca Agnello & Gilles Dufrénot & Ricardo M. Sousa, 2012. "Adjusting the U.S. Fiscal Policy for Asset Prices: Evidence from a TVP-MS Framework," NIPE Working Papers 20/2012, NIPE - Universidade do Minho.

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